Wavelet Toolbox

Wavelet
Toolbox™ provides functions and apps for analyzing and synthesizing signals and images.
The toolbox includes algorithms for continuous wavelet analysis, wavelet coherence,
synchrosqueezing, and data-adaptive time-frequency analysis. The toolbox also includes apps
and functions for decimated and nondecimated discrete wavelet analysis of signals and
images, including wavelet packets and dual-tree transforms.

Using continuous wavelet analysis, you can study the way spectral features evolve over
time, identify common time-varying patterns in two signals, and perform time-localized
filtering. Using discrete wavelet analysis, you can analyze signals and images at different
resolutions to detect changepoints, discontinuities, and other events not readily visible in
raw data. You can compare signal statistics on multiple scales, and perform fractal analysis
of data to reveal hidden patterns.

With Wavelet
Toolbox you can obtain a sparse representation of data, useful for denoising or
compressing the data while preserving important features. Many toolbox functions support
C/C++ code generation for desktop prototyping and embedded system deployment.